Does good AI content exist?

New model capabilities drive content trends.

A widely cited 2023 paper described the AI models used by consultants as “jagged” — well-trained on some subjects, lacking in others. Gen AI content is the same, resulting in online trends that are narrowly focused on a few styles or scenes that a new model does well.

DALL-E 3 / Bing Image Creator (October 2023) - DE3’s then-novel capability to freely reproduce every copyrighted character and celebrity under the sun led to hordes of familiar characters appearing in 9/11 memes and other shock-value scenarios. But the model wasn’t good at creating many characters who weren’t famous, suffering from serious same-face syndrome and inconsistent features.

Veo 3 (May 2025) - Accompanied by a round of Bigfoot influencer videos, which played to its strengths (selfie-stick/influencer footage) while avoiding its weaknesses (it couldn't stop morphing human faces, but had a consistent idea of what Bigfoot looked like).

Sora 2 (September 2025) - While Sora 2 was the most capable video model to date, it still suffered from blurring, morphing, and inconsistent custom characters. All the big Sora 2 trends — Ring doorbell spam, night vision footage — used low-quality or distorted visuals to conceal these faults.

Seedance 2 (February 2026) - Bytedance's video model is a notable step up in prompt comprehension and general polish, but still struggles to keep characters and spaces consistent. Seedance videos often fall back on hyperactive editing (because only a few seconds of the gen are usable) or simpler non-human characters that will remain consistent (as in the CCTV propaganda series about Iran).

There are two selection effects — prompters playing to the model’s strengths and regenerating 100 times to get a good result — that lead viewers to assume models are capable of more than they are. Really, they heavily favor the styles best represented in their training data. They often reproduce characters like Geralt from The Witcher, or objects like Game of Thrones’ Iron Throne, without anyone asking for them; George R.R. Martin could probably sue over image models as well as chatbot output. 

AI is successfully colonizing gaming content.

A February video of Warhammer 40K space marines doing cute animations in the menus of the gacha game Arknights: Endfield did the rounds on Discord and became a top post on the 40K meme subreddit r/grimdank. Some viewers asked where they could download the mod, only to be informed it was AI (probably Seedance). We saw someone post it to a gaming Discord to positive reactions, then delete it after being told it was AI.

There’s already a world of video game mods made “for the Vine” — impractical mods that are funny to feature in shortform video or a Discord bit. They might reach 5M views and 1000 downloads. And there are already influencers who take advantage of this gap between viewing and playing interest. The Minecrafter Fingees amassed more than 1M subs with videos showcasing Minecraft mods that don’t really exist; they seem to be a series of machinima shots stitched together with narration.

As AI improves, it will increasingly encroach on older, accepted forms of video fakery on the internet. The fact that the older fakes were higher quality won’t resonate with many viewers who couldn’t spot the fake in the first place. 

Old AI styles persist.

One of the original AI trends from 2023 was creating video slideshows of “80s dark fantasy” visuals made with the image generator Midjourney. The breakthrough video (since deleted and reuploaded) has all the markers of earlier AI creations — rubbery faces, nonsensical armor and architecture — but also an uncanny draw as a kind of false memory.

The trend is unusual because “dark fantasy” images, which are positioned as cinematic stills, don’t resemble real shots from ‘80s fantasy movies like Excalibur or Dragonslayer. Instead they result from some confusion of colorful concept art, staged publicity stills, and movie screengrabs that really exist only in the weights of the Midjourney model. Some accounts still do numbers posting fantasy videos in the distinctive old Midjourney style in 2026.

The distinctive look and feel of particular AI models is one of the few sources of brand loyalty in the space, where the big models continually leapfrog each other in benchmarks. At this point, Midjourney is well behind the state of the art in prompt comprehension and general realism, but it somehow still has the best dynamic range and style understanding, and owns a look that other models struggle to replicate.

There’s a flip side to the jaggedness mentioned above: newer models sometimes lose the capabilities of older ones. A note in the model card for GPT-Image 1.5, for example, mentions that it's sometimes worse than its predecessor: “The ability to generate some specific art styles has regressed from the previous version.”

Spotting AI fakes has devolved into context clues.

It used to be easy to count the extra fingers or malformed text in an AI forgery. Today, AI videos in some genres look good enough to fool almost anyone. On the booming r/isthisAI subreddit (3.7M subs), commenters often rush to identify every photo as AI, pointing out errors that don’t exist, harping on dubious context clues (“no one would take a dating profile pic on public transit”), or relying on faulty AI detectors.

Even users on the parody subreddit r/isthisAIcirclejerk, which was set up to mock the rubes on r/isthisAI for falling for obvious AI videos, themselves confidently misdiagnose real videos as AI creations.

In addition to inspiring a boom in forensic analysis of popular posts, these advances in AI tech have led to an awkward reality: to spot AI videos, you need to mess around with video-gen models yourself. AI users know that the drunk uncle video above is almost certainly real, as there are too many consistent faces in frame, the shot is longer than a typical 15-second AI creation, and it’s too smooth to be stitched together from multiple takes.

One possibility: will real influencers shift away from the most fake-able content? If viewers are fed up with fake influencers speaking or dancing 10 seconds at a time, will humans turn back to longform out of necessity?

SaaS AI models seem to decline in quality after launch.

This is a very loud complaint within AI communities that often doesn’t travel outside of them. In larger debates about AI content on the internet, you often hear AI guys say “this is the worst it’ll ever be.” But users in communities like r/chatGPT or r/Bard vehemently state the exact opposite — even if the underlying tech keeps improving, the actual products they use degrade visibly after launch.

This argument often comes off as conspiratorial when you see users posting about behind-the-scenes nerfs ruining their existing workflows. (Quantization, a method of reducing model size by reducing precision, is the usual boogeyman.) But it’s clearly true when you look at the launch of Sora 2. The service launched with a wide-open policy on copyrighted IP and a large (but unspecified) daily generation limit, then swiftly backpedaled on both. Soon users complained that the content filters blocked not just copyrighted characters but everything with a superficial resemblance to them; paid users were restricted to 30 generations a day, then told that 15-second generations would count as two generations, effectively halving the quota again.

There was also an apparent decline in prompt comprehension and video quality — though, as AI companies rarely disclose back-end changes, it’s impossible to confirm the details. But the visible backpedaling on services and the strain to free up compute at all top AI labs leaves users in a state of paranoia about invisible nerfs being implemented behind the scenes. They have good reason to be suspicious: recently, OpenAI rival Anthropic actually did confess to making changes that degraded their Claude's performance until users complained en masse.

The generative process remains painfully random.

Generating videos is much easier than filming them. But viewers often underestimate how annoying it is to wrangle current AI models to maintain a consistent look over multiple shots. “Is the whole thing one massive prompt,” a viewer asked on a recent 70-second AI video. “Every clip requires to first generate [a] starting frame image, then generate the video clip, then do this 500 times for a video like this,” someone informed them.

One of the most common giveaways that a video is AI-generated is a sub-15-second running time, which is the usual limit for coherence on models like Seedance 2.0 or Sora 2. (Clips often start losing coherence sooner, which is why AI videos are often quickly edited.) Longer AI videos require the generation of many clips with a matching style and likeness, leading to escalating difficulty and frustration as users fight against the “slot machine” randomness of the generator.

While various tools exist to try to patch up consistency (deepfakes for facial consistency, object removal and substitution workflows), the difficulty of using them in concert pushes out everyone but dedicated hobbyists and professionals. There’s also the sheer cost of generation: current leader Seedance 2.0 can cost more than 20 cents per second for 720p output, and the generation can often have continuity errors or spatial misunderstandings that render it unusable.

In communities like the Banodoco Discord — one of the primary resource hubs for open-source AI video enthusiasts — many think that a complex personal tech stack will be necessary for creatives who want to stand out from the sea of slop. A moderator wrote: “agentic video will soon mean that a music video that looks as good as 99% of existing videos will be pretty much automatable very soon. In this world, doing stuff that’s out of distribution weird…will be key.”

This is a reformulation of the common criticism of AI — generations have no value because anyone can type in a prompt and get something similar. But the flip side is that AI is also making it easier to roll your own models and chain tools together in local workflows that produce odd, distinctive visuals.

People absolutely loathe some forms of AI content.

While many anti-AI viewers have a few carveouts for “good” AI (Vtubers who established themselves early in the boom, artists who create models from their own work, Homer Simpson’s cover of “Starlight”), there are also content formats so debased that even AI promoters condemn them. 

Outside of actually illegal or harassing material, the most disliked forms of AI content seem to be fake history and science videos. These are often recommended by “faceless channel” gurus as an automatable niche where 1) production values are low and skew toward stock footage and slideshows, 2) many viewers use them as background noise and don’t pay close attention, and 3) you can easily hit high running times of 30 minutes to an hour, which are seen as a desirable length for monetization.

Due to the educational nature of these videos, the encroachment of automated and hallucination-prone channels has led to a huge number of furious responses from legitimate creators working in those fields, who now have to contend with competitors that can outproduce them while playing fast and loose with the truth.